{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "from collections import defaultdict\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "import json\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"cm\"\n",
    "\n",
    "matplotlib.rcParams[\"pdf.fonttype\"] = 42\n",
    "matplotlib.rcParams[\"ps.fonttype\"] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gate_k</th>\n",
       "      <th>k</th>\n",
       "      <th>version</th>\n",
       "      <th>task</th>\n",
       "      <th>model</th>\n",
       "      <th>score</th>\n",
       "      <th>num_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.459439</td>\n",
       "      <td>7272665088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.441243</td>\n",
       "      <td>7272665088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.446550</td>\n",
       "      <td>7272665088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.462472</td>\n",
       "      <td>7334531072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.510235</td>\n",
       "      <td>7293636608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.465504</td>\n",
       "      <td>7293636608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.463988</td>\n",
       "      <td>7293636608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.490523</td>\n",
       "      <td>7397445632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.556482</td>\n",
       "      <td>7335579648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.463230</td>\n",
       "      <td>7335579648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.482183</td>\n",
       "      <td>7335579648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.550417</td>\n",
       "      <td>7523274752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.598180</td>\n",
       "      <td>7419465728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.484458</td>\n",
       "      <td>7419465728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.496588</td>\n",
       "      <td>7419465728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.586808</td>\n",
       "      <td>7774932992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.630023</td>\n",
       "      <td>7587237888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.502654</td>\n",
       "      <td>7587237888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.492039</td>\n",
       "      <td>7587237888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8</td>\n",
       "      <td>128</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.637604</td>\n",
       "      <td>8278249472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.662623</td>\n",
       "      <td>7922782208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.505686</td>\n",
       "      <td>7922782208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.489007</td>\n",
       "      <td>7922782208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8</td>\n",
       "      <td>256</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.655800</td>\n",
       "      <td>9284882432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8</td>\n",
       "      <td>384</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.675512</td>\n",
       "      <td>8258326528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>8</td>\n",
       "      <td>384</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.529947</td>\n",
       "      <td>8258326528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>384</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.489765</td>\n",
       "      <td>8258326528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>384</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.682335</td>\n",
       "      <td>10291515392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>1</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;1}$</td>\n",
       "      <td>0.686126</td>\n",
       "      <td>8593870848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>2</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;2}$</td>\n",
       "      <td>0.527672</td>\n",
       "      <td>8593870848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;3}$</td>\n",
       "      <td>0.493556</td>\n",
       "      <td>8593870848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8</td>\n",
       "      <td>512</td>\n",
       "      <td>4</td>\n",
       "      <td>gsm8k</td>\n",
       "      <td>$M_{0;123}$</td>\n",
       "      <td>0.678544</td>\n",
       "      <td>11298148352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    gate_k    k  version   task        model     score   num_params\n",
       "0        8    8        1  gsm8k    $M_{0;1}$  0.459439   7272665088\n",
       "1        8    8        2  gsm8k    $M_{0;2}$  0.441243   7272665088\n",
       "2        8    8        3  gsm8k    $M_{0;3}$  0.446550   7272665088\n",
       "3        8    8        4  gsm8k  $M_{0;123}$  0.462472   7334531072\n",
       "4        8   16        1  gsm8k    $M_{0;1}$  0.510235   7293636608\n",
       "5        8   16        2  gsm8k    $M_{0;2}$  0.465504   7293636608\n",
       "6        8   16        3  gsm8k    $M_{0;3}$  0.463988   7293636608\n",
       "7        8   16        4  gsm8k  $M_{0;123}$  0.490523   7397445632\n",
       "8        8   32        1  gsm8k    $M_{0;1}$  0.556482   7335579648\n",
       "9        8   32        2  gsm8k    $M_{0;2}$  0.463230   7335579648\n",
       "10       8   32        3  gsm8k    $M_{0;3}$  0.482183   7335579648\n",
       "11       8   32        4  gsm8k  $M_{0;123}$  0.550417   7523274752\n",
       "12       8   64        1  gsm8k    $M_{0;1}$  0.598180   7419465728\n",
       "13       8   64        2  gsm8k    $M_{0;2}$  0.484458   7419465728\n",
       "14       8   64        3  gsm8k    $M_{0;3}$  0.496588   7419465728\n",
       "15       8   64        4  gsm8k  $M_{0;123}$  0.586808   7774932992\n",
       "16       8  128        1  gsm8k    $M_{0;1}$  0.630023   7587237888\n",
       "17       8  128        2  gsm8k    $M_{0;2}$  0.502654   7587237888\n",
       "18       8  128        3  gsm8k    $M_{0;3}$  0.492039   7587237888\n",
       "19       8  128        4  gsm8k  $M_{0;123}$  0.637604   8278249472\n",
       "20       8  256        1  gsm8k    $M_{0;1}$  0.662623   7922782208\n",
       "21       8  256        2  gsm8k    $M_{0;2}$  0.505686   7922782208\n",
       "22       8  256        3  gsm8k    $M_{0;3}$  0.489007   7922782208\n",
       "23       8  256        4  gsm8k  $M_{0;123}$  0.655800   9284882432\n",
       "24       8  384        1  gsm8k    $M_{0;1}$  0.675512   8258326528\n",
       "25       8  384        2  gsm8k    $M_{0;2}$  0.529947   8258326528\n",
       "26       8  384        3  gsm8k    $M_{0;3}$  0.489765   8258326528\n",
       "27       8  384        4  gsm8k  $M_{0;123}$  0.682335  10291515392\n",
       "28       8  512        1  gsm8k    $M_{0;1}$  0.686126   8593870848\n",
       "29       8  512        2  gsm8k    $M_{0;2}$  0.527672   8593870848\n",
       "30       8  512        3  gsm8k    $M_{0;3}$  0.493556   8593870848\n",
       "31       8  512        4  gsm8k  $M_{0;123}$  0.678544  11298148352"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MODEL_NAMES = {1: \"$M_{0;1}$\", 2: \"$M_{0;2}$\", 3: \"$M_{0;3}$\", 4: \"$M_{0;123}$\"}\n",
    "output_dir = \"/workspace/svd_subspace/outputs/llama\"\n",
    "results = defaultdict(list)\n",
    "for gate_k in [8]:\n",
    "    for k in [8, 16, 32, 64, 128, 256, 384, 512]:\n",
    "        for version in [1, 2, 3, 4]:\n",
    "            for task in [\"gsm8k\"]:\n",
    "                result_dir = os.path.join(\n",
    "                    output_dir,\n",
    "                    f\"gate_k-{gate_k}_k-{k}\",\n",
    "                    f\"version_{version}\",\n",
    "                    f\"{task}.json\",\n",
    "                )\n",
    "                # get the first file in the file subdir of result_dir\n",
    "                if not os.path.isdir(result_dir):\n",
    "                    print(f\"missing {result_dir}\")\n",
    "                    continue\n",
    "                result_dir = os.path.join(result_dir, os.listdir(result_dir)[0])\n",
    "                result_file = os.path.join(result_dir, os.listdir(result_dir)[0])\n",
    "\n",
    "                results[\"gate_k\"].append(gate_k)\n",
    "                results[\"k\"].append(k)\n",
    "                results[\"version\"].append(version)\n",
    "                results[\"task\"].append(task)\n",
    "                results[\"model\"].append(MODEL_NAMES[version])\n",
    "\n",
    "                if task == \"gsm8k\":\n",
    "                    with open(result_file, \"r\") as f:\n",
    "                        data = json.load(f)\n",
    "                    results[\"score\"].append(\n",
    "                        data[\"results\"][\"gsm8k\"][\"exact_match,flexible-extract\"]\n",
    "                    )\n",
    "\n",
    "                with open(\n",
    "                    os.path.join(\n",
    "                        output_dir,\n",
    "                        f\"gate_k-{gate_k}_k-{k}\",\n",
    "                        f\"version_{version}\",\n",
    "                        \"model_info.json\",\n",
    "                    ),\n",
    "                    \"r\",\n",
    "                ) as f:\n",
    "                    model_info = json.load(f)\n",
    "                results[\"num_params\"].append(model_info[\"model_info\"][\"all_params\"])\n",
    "results = pd.DataFrame(results)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n",
      "'created' timestamp seems very low; regarding as unix timestamp\n",
      "'modified' timestamp seems very low; regarding as unix timestamp\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "palette = [\"#7EA6E0\", \"#97D077\", \"#EA6B66\", \"#FFB570\", \"#CCCCCC\"]\n",
    "plt.rcParams.update({\"font.size\": 16})\n",
    "\n",
    "# Assuming 'results' is a DataFrame with columns 'k', 'score', and 'model'\n",
    "models = results[\"model\"].unique()\n",
    "\n",
    "for model_idx, model in enumerate(models):\n",
    "    subset = results[(results[\"model\"] == model) & (results[\"task\"] == \"gsm8k\")]\n",
    "    plt.plot(\n",
    "        subset[\"k\"], subset[\"score\"], linestyle=\"--\", label=model, c=palette[model_idx]\n",
    "    )\n",
    "    plt.scatter(\n",
    "        subset[\"k\"],\n",
    "        subset[\"score\"],\n",
    "        s=subset[\"num_params\"] / 700_000_000 * 3,\n",
    "        c=palette[model_idx],\n",
    "    )\n",
    "\n",
    "# plot vertical lines of k\n",
    "# for k in results[\"k\"].unique():\n",
    "# plt.axvline(x=k, color=\"gray\", linestyle=\"-.\", linewidth=0.5)\n",
    "\n",
    "plt.xlabel(\"rank of local experts $k$\")\n",
    "plt.ylabel(\"GSM8K Match Score\")\n",
    "plt.legend(loc=\"center right\", fontsize=14, bbox_to_anchor=(1, 0.55))\n",
    "\n",
    "# plot another two lines\n",
    "# 1. 71.49% is the score of the best model\n",
    "# 2. 38.81% is the score of the base model\n",
    "plt.axhline(y=0.7149, color=palette[0], linewidth=1, linestyle=\"-\")\n",
    "plt.text(0, 0.7149 - 0.02, \"GSM8K Score of $M_1$\", fontsize=14, color=\"black\")\n",
    "# plt.axhline(y=0.3881, color=\"black\", linestyle=\"-\", linewidth=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"mistral_gsm8k.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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